A Foundation for Spatial Thinking: Towards a Threshold Concept Framework in GIScience and its Implications for STEM Education
Notice bibliographique
Résumé
Geographic Information Science (GIScience) is a fairly modern, rapidly emerging multidisciplinary field, addressing the theories and concepts behind the spatial technology called Geographic Information Systems (GIS). With the proliferation of this technology, the demand for GIS professionals has also increased, as has pressure to support their competency in the community of practice. This study investigates a framework of threshold concepts to provide insight into the learning process for distinctive ways of thinking and practising within GIScience. Despite some theoretical investigations of this framework, no empirical studies have explored learner insights on this topic and its implications. The main goal of the study is to investigate empirical evidence of a threshold concept framework in GIScience and its potential implications for Science, Technology, Engineering, and Mathematics (STEM) education. \n \nThe investigation relied on data collected through survey questionnaires and personal interviews, administered in an introductory GIS course at the University of Waterloo in Ontario, Canada. The qualitative assessment of the study was based on a phenomenographic approach to examine different ways in which students experience GIS learning. First, threshold concepts were explored and examined based on their transformative, irreversible, integrative, bounded, and troublesome characteristics. In addition, statistical analyses were employed to identify important factors promoting student proficiency in GIScience. These factors were further examined with respect to variations in students’ discipline-specific ways of thinking and practising. The implications for STEM education were also discussed, in terms of shared misconceptions, spatial thinking abilities, and academic and career competencies. \n \nFindings from this study suggest that the most prominent threshold concepts perceived by GIScience students are map projections and advantages and disadvantages of raster and vector data models, which are likely to open up new and previously inaccessible ways of thinking (i.e., ways of looking at a map). Important factors for students to acquire an understanding of such concepts were also identified, including academic preparedness, educational status, major field of study, type of academic background, ArcGIS software experience, GIS learning resource, and prior subject learning experience in mathematics, GIS, programming, or computer science. The implications include enhanced spatial thinking ability, as well as creativity, critical thinking, and problem-solving skills, all of which can help to promote interest and self-confidence in pursuing STEM fields. Overall, results from this study offer valuable insights for enhancing the efficacy of teaching and learning in GIScience.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,036 |
| Communication savante | 0,011 | 0,015 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».